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Korean J Obes 2011; 20(2): 75-83

Published online June 1, 2011

Copyright © Korean Society for the Study of Obesity.

Visceral Fat Measured by the Electrical Impedance Analysis Method is a Reliable Predictor of Insulin Resistance

Min Kyeong Kim†, In Suk Lee(1)†, Hye Won Jang, Kyong Hye Joung, Koon Soon Kim, Hyun Jin Kim, Tae Young Lee(2), Young Kun Kim, Bon Jeong Ku*

Department of Internal Medicine, Chungnam National University School of Medicine; Department of Internal Medicine, The Catholic University College of Medicine(1),Department of Preventive Medicine, Chungnam National University School of Medicine(2)

Abstract

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Background: The aim of this study was to investigate whether visceral fat measured by the electrical impedance analysis (EIA) method could serve as predictor of insulin resistance (IR).
Methods: We evaluated a total of 1993 subjects from the Korean Rural Genomic Cohort Study in a population-based cross-sectional study. Insulin sensitivity was measured by fasting insulin, Homeostasis model for insulin resistance (HOMA-IR), and the quantitative insulin sensitivity check index (QUICKI). Body composition was measured by EIA to determine total body fat and lean body mass.
Results: Visceral fat was most significantly related to IR among various clinical parameters. The cutoff values for fasting insulin, HOMA-IR, and QUICKI to define IR were 7.25 μU/mL (sensitivity 69.3%, specificity 63.2%), 1.70 (sensitivity 68.5%, specificity 66.4%), and 0.352 (sensitivity 66.5%, specificity 68.6%) (P < 0.001), respectively. The area under the receiver operating characteristic (ROC) curve (95% CI) of visceral fat to predict IR was 0.730 (0.706~0.753).
Conclusion: This study suggests that visceral fat measured by EIA method was most significantly related to and sufficient to predict IR.

Keywords: Intra-abdominal fat, Insulin resistance, Electrical impedance analysis

Figures

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Fig. 1. ROC curves for fasting serum insulin, HOMA-IR, and QUICKI for increased metabolic syndrome. The cutoff values for fasting serum insulin, HOMA-IR, and QUICKI are 7.25 ?U/mL (sensitivity 69.3%, specificity 63.2%), 1.70 (sensitivity 68.5%, specificity 66.4%), and 0.352 (sensitivity 66.5%, specificity 68.6%) (P < 0.001, respectively). The areas under the ROC curves (95% CI) for the parameters are 0.712 (0.687-0.737), 0.727 (0.702-0.752), and 0.727 (0.702-0.752), respectively.
Fig. 2. ROC curves for visceral fat for predicting insulin resistance. The cutoff value for visceral fat is 2.35 kg (sensitivity 56.0%, specificity 78.1%) (P < 0.001). The area under the ROC curve (95% CI) for the parameter is 0.730 (0.706-0.753).

Tables

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The baseline characteristics of the subjects



Correlation between surrogate markers of insulin resistance and metabolic risk factors



Prevalence and odds ratios (OR) for metabolic syndrome according to the quartiles of fasting insulin, HOMA-IR, and QUICKI


References

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  1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37(12):1595-607.
    CrossRef
  2. Reaven GM. Role of insulin resistance in human disease (syndrome X): an expanded definition. Annu Rev Med 1993;44:121-31.
    Pubmed CrossRef
  3. DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, atherosclerotic cardiovascular disease. Diabetes Care 1991;14(3):173 -94.
    CrossRef
  4. Greenfield MS, Doberne L, Kraemer F, Tobey T, Reaven G. Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp. Diabetes 1981;30(5):387-92.
    Pubmed CrossRef
  5. Bergman RN, Prager R, Volund A, Olefsky JM. Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest 1987;79(3):790-800.
    Pubmed KoreaMed CrossRef
  6. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28(7):412-9.
    Pubmed CrossRef
  7. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity:studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care 2000;23(1):57-63.
    Pubmed CrossRef
  8. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85(7):2402-10.
    Pubmed CrossRef
  9. Banerji MA, Faridi N, Atluri R, Chaiken RL, Lebovitz HE. Body composition, visceral fat, leptin, insulin resistance in Asian Indian men. J Clin Endocrinol Metab 1999;84(1):137-44.
    Pubmed CrossRef
  10. Yoshizumi T, Nakamura T, Yamane M, Islam AHMW, Menju M, Yamasaki K, et al. Abdominal fat: Standardized technique for measurement at CT. Radiology 1999;211(1):283-6.
    Pubmed CrossRef
  11. Ryo M, Maeda K, Onda T, Katashima M, Okumiya A, Nishida M, et al. A new simple method for the measurement of visceral fat accumulation by bioelectrical impedance. Diabetes Care 2005;28(2):451-3.
    Pubmed CrossRef
  12. Hrebicek J, Janout V, Malincikova J, Horakova D, Cizek L. Detection of insulin resistance by simple quantitative insulin sensitivity check index QUICKI for epidemiological assessment and prevention. J Clin Endocrinol Metab 2002;87(1):144-7.
    Pubmed CrossRef
  13. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3(1):32-5.
    CrossRef
  14. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 1979;237(3):E214-23.
    Pubmed
  15. Ascaso JF, Pardo S, Real JT, Lorente RI, Priego A, Carmena R. Diagnosing insulin resistance by simple quantitative methods in subjects with normal glucose metabolism. Diabetes Care 2003;26(12):3320-5.
    Pubmed CrossRef
  16. Yeni-Komshian H, Carantoni M, Abbasi F, Reaven GM. Relationship between several surrogate estimates of insulin resistance and quantification of insulin -mediated glucose disposal in 490 healthy nondiabetic volunteers. Diabetes Care 2000;23(2):171-5.
    Pubmed CrossRef
  17. Ryu S, Sung KC, Chang Y, Lee WY, Rhee EJ. Spectrum of insulin sensitivity in the Korean population. Metabolism 2005;54(12):1644-51.
    Pubmed CrossRef
  18. Lee S, Muniyappa R, Yan X, Chen H, Yue LQ, Hong EG, et al. Comparison between surrogate indexes of insulin sensitivity and resistance and hyperinsulinemic euglycemic clamp estimates in mice. Am J Physiol-Endoc M 2008;294(2):E261-70.
  19. Nagaretani H, Nishizawa H, Nakamura T, Kishida K, Funahashi T, Kuriyama H, et al. Visceral fat is a major contributor for multiple risk factor clustering in Japanese men with impaired glucose tolerance. Diabetes Care 2001;24(12):2127-33.
    Pubmed CrossRef
  20. Kim SK, Kim HJ, Hur KY, Choi SH, Ahn CW, Lim SK, et al. Visceral fat thickness measured by-ultrasonography can estimate not only visceral obesity but also risks of cardiovascular and metabolic diseases. Am J Clin Nutr 2004;79(4):593-9.
    Pubmed
  21. Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, Kahn SE, et al. Visceral adiposity and the risk of impaired glucose tolerance - A prospective study among Japanese Americans. Diabetes Care 2003;26(3):650-5.
    Pubmed CrossRef
  22. Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994;73(7):460-8.
    CrossRef
  23. Kuo CS, Hwu CM, Chiang SC, Hsiao LC, Weih MJ, Kao WY, et al. Waist circumference predicts insulin resistance in offspring of diabetic patients. Diabetes Nutr Metab 2002;15(2):101-8.
    Pubmed
  24. Poirier P, Lemieux I, Mauriege P, Dewailly E, Blanchet C, Bergeron J, et al. Impact of waist circumference on the relationship between blood pressure and insulin - The Quebec Health Survey. Hypertension 2005;45(3):363-7.
    Pubmed CrossRef
  25. Borkan GA, Gerzof SG, Robbins AH, Hults DE, Silbert CK, Silbert JE. Assessment of abdominal fat content by computed tomography. Am J Clin Nutr 1982;36(1):172-7.
    Pubmed
  26. Enzi G, Gasparo M, Biondetti PR, Fiore D, Semisa M, Zurlo F. Subcutaneous and visceral fat distribution according to sex, age, overweight, evaluated by computed tomography. Am J Clin Nutr 1986;44(6):739-46.
    Pubmed
  27. Staten MA, Totty WG, Kohrt WM. Measurement of fat distribution by magnetic resonance imaging. Invest Radiol 1989;24(5):345-9.
    Pubmed CrossRef
  28. Armellini F, Zamboni M, Rigo L, Bergamo-Andreis IA, Robbi R, De Marchi M, et al. Sonography detection of small intra-abdominal fat variations. Int J Obes 1991;15(12):847-52.
    Pubmed
  29. Lim S, Park KS, Lee HK, Cho SI. Changes in the characteristics of metabolic syndrome in Korea over the period 1998-2001 as determined by Korean National Health and Nutrition Examination Surveys. Diabetes Care 2005;28(7):1810-2.
    Pubmed CrossRef
  30. Gradmark AM, Rydh A, Renstrom F, De Lucia-Rolfe E, Sleigh A, Nordstrom P, et al. Computed tomography-based validation of abdominal adiposity measurements from ultrasonography, dual-energy X-ray absorptiometry and anthropometry. Br J Nutr 2010;104(4):582-8.
    Pubmed CrossRef

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